Several recent studies have shown to reduce the computational workload during the evaluation process through machine learning (ML) techniques, including artificial neural networks [1]}, support vector regression [2]}, and deep learning (DL) networks [3]}, [4]}, [5]}, [6]}. These methods map the non-linear relationship between the geometric parameters and antenna characteristics using data from EM solvers. Once trained, the design process is significantly accelerated with the surrogate model replacing the EM solver for rapidly generating the antenna behaviors for any given set of geometry parameters. In this context, deep generative adversarial networks (GANs) have emerged as a preferred DL technique to solve a wide variety of EM problems [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
A GAN works as a zero-sum game between two deep networks: generator and discriminator. Its objective is to implicitly learn the probabilistic distribution of a set of training samples and, subsequently, create samples of the distribution during the prediction stage [14]}. The generator produces samples of a distribution from the training data while the discriminator assesses the samples and decides if they are real or fake (produced by the generator). The primary advantage of using the GAN framework with two competing neural networks is that the GAN is semi-supervised
and requires smaller, less diverse training data set
[15]}.
